Identifying vegetation attributes from lidar data

ABSTRACT

Aspects of the present invention are directed at using LiDAR data to identify attributes of vegetation. In this regard, a method is provided that identifies the location of individual items of vegetation from raw LiDAR data. In one embodiment, the method includes selecting a coordinate position represented in the LiDAR data that generated a return signal. Then, a determination is made regarding whether the selected coordinate position is inside a geographic area allocated to a previously identified item of vegetation. If the selected coordinate position is not within a geographic area allocated to a previously identified item of vegetation, the method determines that the selected coordinate position is associated with a new item of vegetation. In this instance, a digital representation of the new item of vegetation is generated.

BACKGROUND

A long-standing need exists for biologists, forest managers, and othersto have information that characterizes a set of vegetation, such as astand of trees. Traditionally, attributes of a sample of the vegetationare manually obtained and extrapolated to a larger set of vegetation.For example, sampling may be performed to assess the vegetation'sheight, volume, age, biomass, and species, among other attributes. Thisinformation that characterizes the attributes of the vegetation may beused in a number of different ways. For example, the sample data may beused to quantify the inventory of raw materials that are available forharvest. By way of another example, by comparing attributes of a sampleset of vegetation over time, one may determine whether a disease iscompromising the health of the vegetation.

Unfortunately, extrapolating sample data to a larger set may notaccurately reflect the actual attributes of the vegetation. In thisregard, the species and other vegetation attributes may depend on anumber of different factors that are highly variable even in nearbygeographic locations. As a result, biologists, forest managers, andothers may not have information that accurately characterizes theattributes of vegetation.

Advancements in airborne and satellite laser scanning technology providean opportunity to obtain more accurate information about the attributesof vegetation. In this regard, Light Detection and Ranging (“LiDAR”) isan optical remote scanning technology used to identify distances toremote targets. For example, a laser pulse may be transmitted from asource location, such as an aircraft or satellite, to a target locationon the ground. The distance to the target location may be quantified bymeasuring the time delay between transmission of the pulse and receiptof one or more reflected return signals. Moreover, the intensity of areflected return signal may provide information about the attributes ofthe target. In this regard, a target on the ground will reflect returnsignals in response to a laser pulse with varying amounts of intensity.For example, a species of vegetation with a high number of leaves will,on average, reflect return signals with higher intensities thanvegetation with a smaller number of leaves.

LiDAR optical remote scanning technology has attributes that make itwell-suited for identifying the attributes of vegetation. For example,the wavelengths of a LiDAR laser pulse are typically produced in theultraviolet, visible, or near infrared areas of the electromagneticspectrum. These short wavelengths are very accurate in identifying thehorizontal and vertical location of leaves, branches, etc. Also, LiDARoffers the ability to perform high sampling intensity, extensive aerialcoverage, as well as the ability to penetrate the top layer of avegetation canopy. In this regard, a single LiDAR pulse transmitted totarget vegetation will typically produce a plurality of return signalsthat each provide information about attributes of the vegetation.

A drawback of existing systems is an inability to identify the locationof individual trees, bushes, and other vegetation that is scanned usingLiDAR instrumentation. For example, raw LiDAR data may be collected inwhich a forest is scanned at a high sampling intensity sufficient toproduce data that describes the position and reflective attributes ofindividual items of vegetation. It would be beneficial to have a systemin which the raw LiDAR data is processed in order to identify thelocation of the individual items of vegetation.

It would also be beneficial to have a system capable of identifyingvarious attributes of vegetation from raw LiDAR data. For example, witha high enough sampling rate, the shape and other properties of a tree'scrown, branches, and leaves may be discernible. If this type ofinformation was discernable, computer systems may be able to identifythe species of individual items of vegetation.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

Aspects of the present invention are directed at using LiDAR data toidentify attributes of vegetation. In this regard, a method is providedthat identifies the location of individual items of vegetation from rawLiDAR data. In one embodiment, the method includes selecting acoordinate position represented in the LiDAR data that generated areturn signal. Then, a determination is made regarding whether theselected coordinate position is inside a geographic area allocated to apreviously identified item of vegetation. If the selected coordinateposition is not within a geographic area allocated to a previouslyidentified item of vegetation, the method determines that the selectedcoordinate position is associated with a new item of vegetation. In thisinstance, a digital representation of the new item of vegetation isgenerated.

DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same become betterunderstood by reference to the following detailed description, whentaken in conjunction with the accompanying drawings, wherein:

FIG. 1 depicts components of a computer that may be used to implementaspects of the present invention;

FIG. 2 depicts an exemplary crown identification routine for identifyingthe location and attributes of a crown associated with an item ofvegetation in accordance with one embodiment of the present invention;

FIG. 3 depicts a sample set of LiDAR data that may be used to illustrateaspects of the present invention;

FIG. 4 depicts a digital representation of a tree that may be used toillustrate aspects of the present invention;

FIG. 5 depicts a sample tree list data file with information describingthe attributes of vegetation that is scanned with LiDAR instrumentation;

FIG. 6 depicts an exemplary species identification routine thatidentifies the species of an individual item of vegetation in accordancewith another embodiment of the present invention; and

FIG. 7 depicts an exemplary species attribute template that may beemployed to differentiate between species of vegetation in accordancewith another embodiment of the present invention.

DETAILED DESCRIPTION

The present invention may be described in the context ofcomputer-executable instructions, such as program modules being executedby a computer. Generally described, program modules include routines,programs, applications, widgets, objects, components, data structures,and the like, that perform tasks or implement particular abstract datatypes. Moreover, the present invention may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communication network. In adistributed computing environment, program modules may be located onlocal and/or remote computing storage media.

While the present invention will primarily be described in the contextof using raw LiDAR data to identify the attributes of vegetation, thoseskilled in the relevant art and others will recognize that the presentinvention is also applicable in other contexts. For example, aspects ofthe present invention may be implemented using other types of scanningsystems to identify the attributes of vegetation. In any event, thefollowing description first provides a general overview of a computersystem in which aspects of the present invention may be implemented.Then, methods for identifying the location and species of individualitems of vegetation will be described. The illustrative examplesprovided herein are not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Similarly, any steps describedherein may be interchangeable with other steps, or a combination ofsteps, in order to achieve the same result.

Now with reference to FIG. 1, an exemplary computer 100 with componentsthat are capable of implementing aspects of the present invention willbe described. Those skilled in the art and others will recognize thatthe computer 100 may be any one of a variety of devices including, butnot limited to, personal computing devices, server-based computingdevices, mini and mainframe computers, laptops, or other electronicdevices having some type of memory. For ease of illustration and becauseit is not important for an understanding of the present invention, FIG.1 does not show the typical components of many computers, such as akeyboard, a mouse, a printer, a display, etc. However, the computer 100depicted in FIG. 1 includes a processor 102, a memory 104, acomputer-readable medium drive 108 (e.g., disk drive, a hard drive,CD-ROM/DVD-ROM, etc.), that are all communicatively connected to eachother by a communication bus 110. The memory 104 generally comprisesRandom Access Memory (“RAM”), Read-Only Memory (“ROM”), flash memory,and the like.

As illustrated in FIG. 1, the memory 104 stores an operating system 112for controlling the general operation of the computer 100. The operatingsystem 112 may be a general purpose operating system, such as aMicrosoft® operating system, a Linux operating system, or a UNIX®operating system. Alternatively, the operating system 112 may be aspecial purpose operating system designed for non-generic hardware. Inany event, those skilled in the art and others will recognize that theoperating system 112 controls the operation of the computer by, amongother things, managing access to the hardware resources and inputdevices. For example, the operating system 112 performs functions thatallow a program to read data from the computer-readable media drive 108.As described in further detail below, raw LiDAR data may be madeavailable to the computer 100 from the computer-readable media drive108. In this regard, a program installed on the computer 100 mayinteract with the operating system 112 to access LiDAR data from thecomputer-readable media drive 108.

As further depicted in FIG. 1, the memory 104 additionally storesprogram code and data that provides a LiDAR processing application 114.In one embodiment, the LiDAR processing application 114 comprisescomputer-executable instructions that, when executed by the processor102, applies an algorithm to a set of raw LiDAR data to identify thelocation of individual items of vegetation scanned using LiDARinstrumentation. As mentioned previously, LiDAR is an optical remotescanning technology that may be used to identify distances to remotetargets. In this regard, a series of laser pulses may be transmittedfrom an aircraft, satellite, or other source location to targetlocations on the ground. The distance to vegetation impacted with thelaser pulse (leaves, branches, etc.) is determined by measuring the timedelay between transmission of the laser pulse and receipt of a returnsignal. Moreover, the intensity of the return signal varies depending onattributes of the vegetation that is contacted. In one embodiment, theLiDAR processing application 114 uses distance and intensity valuesrepresented in the raw LiDAR data to identify the location of individualitems of vegetation (e.g., trees, plants, etc.) from which the raw LiDARdata was collected. In this regard, an exemplary embodiment of a routineimplemented by the LiDAR processing application 114 that identifies thelocation of individual items of vegetation is described below withreference to FIG. 2.

In another embodiment, the LiDAR processing application 114 comprisescomputer-executable instructions that, when executed, by the processor102, applies an algorithm that identifies the species of an individualitem of vegetation. More specifically, the LiDAR processing application114 implements functionality that identifies attributes of an individualitem of vegetation including, but not limited to, height, crownparameters, branching patterns, among others. When a distinguishingattribute of the vegetation is known, processing is performed toidentify the species of the vegetation. In this regard, an exemplaryembodiment of a routine implemented by the LiDAR processing application114 that is configured to identify species information from LiDAR datais described below with reference to FIG. 6.

As further depicted in FIG. 1, the memory 104 additionally storesprogram code and data that provides a database application 116. Asmentioned previously, the LiDAR processing application 114 may identifycertain vegetation attributes from LiDAR data. In accordance with oneembodiment, the database application 116 is configured to storeinformation that describes these vegetation attributes identified by theLiDAR processing application 114 in the inventory database 118. In thisregard, the database application 116 may generate queries for thepurpose of interacting with the inventory database 118. Accordingly, theinventory database 118 may be populated with a large collection of datathat describes the attributes of vegetation from which LiDAR data wascollected.

FIG. 1 depicts an exemplary architecture for the computer 100 withcomponents that may be used to implement one or more embodiments of thepresent invention. Of course, those skilled in the art and others willappreciate that the computer 100 may include fewer or more componentsthan those shown in FIG. 1. Moreover, those skilled in the art andothers will recognize that while a specific computer configuration andexamples have been described above with reference to FIG. 1, thespecific examples should be construed as illustrative in nature asaspects of the present invention may be implemented in other contextswithout departing from the scope of the claimed subject matter.

Now with reference to FIG. 2, an exemplary crown identification routine200 that identifies the location of individual items of vegetation fromraw LiDAR data will be described. As illustrated in FIG. 2, the crownidentification routine 200 begins at block 202 where pre-processing isperformed to translate raw LiDAR data into a standardized format thatmay be shared. For example, the pre-processing performed, at block 202,may translate raw LiDAR data into a format that adheres to the AmericanSociety of Photogrammetry and Remote Sensing (“ASPRS”) .LAS binary filestandard. In this regard, the ASPRS .LAS file format is a binary fileformat that is configured to store three-dimensional data pointscollected using LiDAR instrumentation. As described in further detailbelow, the .LAS file format includes well-defined records and fieldsthat are readily accessible to software systems implemented by aspectsof the present invention.

For illustrative purposes and by way of example only, a sample set 300of LiDAR data that may be included in an ASPRS .LAS file is depicted inFIG. 3. In this exemplary embodiment, the sample set 300 of LiDAR dataincludes the records 302, 304, and 306 that each correspond to a laserpulse generated from LiDAR instrumentation. The records 302-306 depictedin FIG. 3 are organized into columns that include a return number column308, a location column 310, an intensity column 312, and a ground flagcolumn 314. As mentioned previously, each laser pulse generated fromLiDAR instrumentation may be associated with a plurality of reflectedreturn signals. Accordingly, the return number column 308 identifiesreturn signals based on the chronological order in which the returnsignals were received. In the exemplary sample set 300 of data depictedin FIG. 3, the location column 310 identifies a three-tuple ofcoordinates (e.g., X, Y, and Z) of the location that generated thereturn signal. In accordance with one embodiment, the three-tuple ofcoordinates in the location column 310 adheres to the UniversalTransverse Mercator (“UTM”) coordinate system. In this regard, theGeographic Information System (“GIS”) may be used to map raw LiDAR datato the UTM coordinate system. However, those skilled in the art andothers will recognize that other types of mapping technology may beemployed to identify these coordinate positions without departing fromthe scope of the claimed subject matter.

As further illustrated in FIG. 3, the sample set 300 of LiDAR datadepicted in FIG. 3 includes an intensity column 312 that identifies theintensity of a corresponding return signal. In this regard, theintensity with which a return signal is reflected from a target locationdepends on a number of different factors. More specifically, the amountof surface area contacted by the LiDAR pulse affects the intensityvalue, as well as the physical characteristics of the subject matterthat is contacted. For example, the more surface area that is contactedby the LiDAR pulse, the higher the intensity of the return signal. Also,the data provided in the ground flag column 314 indicates whether theparticular return signal was identified as being the ground or floorbelow a vegetation canopy.

As illustrated in FIG. 3, the pre-processing performed at block 202 togenerate the sample set 300 of data may include translating raw LiDARdata into a well-defined format. Moreover, in the embodiment depicted inFIG. 3, pre-processing is performed to identify return signals that weregenerated from contacting the ground or floor below the vegetationcanopy. As described in further detail below, identifying return signalsthat are reflected from the ground or floor below a vegetation canopymay be used to identify the height of an item of vegetation.

With reference again to FIG. 2, at block 204, coordinate positions thatare within the bounds of a selected polygon are identified. In oneembodiment, aspects of the present invention sequentially processlocations inside a predetermined geographic area (e.g., polygon) beforeother geographic areas are selected for processing. Accordingly, thegeographic area occupied by a selected polygon is compared to thecoordinate positions in a set of raw LiDAR data that generated returnsignals. In this regard, an intersection operation is performed for thepurpose of identifying coordinate positions in a set of LiDAR data thatare within the selected polygon. As described in further detail below,the locations of vegetation within the selected polygon are identifiedbefore other geographic areas are selected.

As further illustrated in FIG. 2, at block 206, coordinate positionsthat generated a return signal within the selected polygon are sortedbased on their absolute height above sea level. In this regard, thecoordinate position identified as being the highest is placed in thefirst position in the sorted data. Similarly, the lowest coordinateposition is placed into the last position in the sorted data. However,since sorting locations based on their absolute height may be performedusing techniques that are generally known in the art, furtherdescription of these techniques will not be described here.

At block 208, a location in the LiDAR data that generated a returnsignal is selected for processing. In one embodiment, aspects of thepresent invention sequentially select locations represented in thesorted data, at block 206, based on the location's absolute height. Inthis regard, the highest location in the sorted data is selected firstwith the lowest location being selected last.

At decision block 210, a determination is made regarding whether thelocation selected at block 208 is below a previously created digitalcrown umbrella. As described in more detail below, the inventiongenerates a digital crown umbrella for each item of vegetation whichrepresents an initial estimation of the area occupied by the vegetation.In this regard, if the selected location is below a previously createddigital crown umbrella, then the result of the test performed at block210 is “YES,” and the crown identification routine 200 proceeds to block214, described in further detail below. Conversely, if the locationselected at block 208 is not under a previously created digital crownumbrella, the crown identification routine 200 determines that theresult of the test performed at block 210 is “NO” and proceeds to block212.

At block 212, a digital crown umbrella is created that represents aninitial estimate of the area occupied by an individual item ofvegetation. If block 212 is reached, the location selected at block 208is identified as being the highest location in an individual item ofvegetation. In this instance, a digital crown umbrella is created sothat all other locations in the LiDAR data may be allocated to anindividual item of vegetation. In this regard, the digital crownumbrella is an initial estimate of the area occupied by an item ofvegetation. However, as described in further detail below, the areaallocated to an individual item of vegetation may be modified as aresult of processing other locations represented in the data.

In accordance with one embodiment, the size of the digital crownumbrella created at block 212 is estimated based on a set of knowninformation. As described above with reference to FIG. 3, data obtainedby aspects of the present invention include an indicator of whichlocation represented in a LiDAR record is associated with the ground orfloor below a vegetation canopy. Moreover, if block 212 is reached, thehighest location that generated a return signal was identified. Thus,the height of an individual item of vegetation may be estimated byidentifying the difference between the highest location of an item ofvegetation that generated a return signal and the ground or floor belowthe vegetation canopy. Those skilled in the art others will recognizethat a strong correlation exists between the height of vegetation andthe size of the vegetation's crown. Thus, the size of the digital crownumbrella may be estimated based on the height of the vegetation, amongother factors.

As further illustrated in FIG. 2, at block 214, a digital branchumbrella, which represents the area occupied by a branch, is created. Ifblock 214 is reached, the location selected at block 208 is below adigital crown umbrella created during a previous iteration of the crownidentification routine 200. Thus, the selected location that generated areturn signal may represent a component of the vegetation, such as abranch, leaf, etc. In this instance, a digital branch umbrella iscreated that potentially extends the area allocated to an item ofvegetation. As mentioned previously, a digital crown umbrella representsan initial estimate of the area occupied by an individual item ofvegetation. However, additional processing of LiDAR data may indicatethat an individual item of vegetation is larger than the initialestimate as represented in the digital crown umbrella. In this instance,the area allocated to an item of vegetation may be expanded to accountfor additional processing of the LiDAR data.

Now with reference to FIG. 4, the relationship between digital crown andbranch umbrellas that may be used to represent an area occupied by anitem of vegetation will be described. For illustrative purposes, a tree400 is depicted in FIG. 4 with three locations 402, 404, and 406 thatwere contacted by a laser pulse. In this example, when location 402 isselected, the crown identification routine 200 generates the digitalcrown umbrella 408 to provide an initial estimate of the area occupiedby the tree 400. Thereafter, when location 404 is selected, adetermination is made that the location 404 is below the digital crownumbrella 408. In this instance, the crown identification routine 200creates the digital branch umbrella 410. Similarly, when location 406 isselected, a determination is made that the location 406 is below thedigital crown umbrella 408 and the crown identification routine 200creates the digital branch umbrella 412. In this example, the digitalbranch umbrella 412 expands the area 414 that was initially allocated tothe tree 400 by aspects of the present invention. In this way, atop-down hierarchical approach is used to initially estimate the areaoccupied by the tree 400 with modifications being performed to enlargethis area, if appropriate.

Again with reference to FIG. 2, a determination is made at decisionblock 216 regarding whether additional locations represented in theLiDAR data will be selected. As mentioned previously, aspects of thepresent invention sequentially select locations represented in LiDARdata that generated a return signal. Typically, all of the locationsrepresented in a file of LiDAR data are selected and processedsequentially. Thus, when each record in a file of LiDAR data has beenselected, the crown identification routine 200 proceeds to block 218,described in further detail below. Conversely, if additional locationswill be selected, the crown identification routine 200 proceeds back toblock 208, and blocks 208-216 repeat until all of the locationsrepresented in the file have been selected.

As further illustrated in FIG. 2, at block 218, a tree list data file iscreated with data that describes attributes of individual items ofvegetation. In this regard, and as described further below withreference to FIG. 5, aspects of the present invention identify certainattributes of each item of vegetation from which LiDAR data wascollected. Significantly, the tree list data file may be used to updatethe contents of a database such as the inventory database 118 (FIG. 1)that tracks an inventory of raw materials available for harvest. Oncethe tree list data file is created, the crown identification routine 200proceeds to block 220, where it terminates.

For illustrative purposes and by way of example only, a section 500 of atree list data file created by aspects of the invention is depicted inFIG. 5. In this exemplary embodiment, the tree list data file includes aplurality of records 502-508 that each correspond to an item ofvegetation. The records 502-508 are organized into columns that includean identifier column 510, a location column 512, a height column 514, aheight to live crown (“HTLC”) column 516, and a diameter at breastheight (“DBH”) column 518. In this regard, the identifier column 510includes a unique numeric identifier for each item of vegetationidentified by the crown identification routine 200. Similar to thedescription provided above with reference to FIG. 3, the location column512 includes a three-tuple of coordinates that identifies the locationof a corresponding item of vegetation. As mentioned previously, theheight of an item of vegetation represented in the height column 514 maybe calculated by identifying the difference between the highest locationthat generates a return signal with the ground or floor below avegetation canopy.

As further illustrated in FIG. 5, the tree list data file 500 includes aHTLC column 516. Those skilled in the art and others will recognize thatan item of vegetation such as a tree will include live branches andleaves on the upper part of the tree. The portion of the tree thatincludes live branches and leaves is typically referred to as a “livecrown.” However, a portion of the tree beginning from the base of thetree will not have live branches or leaves. The distance from the baseof the tree to the live crown is identified in the HTLC column 516.Finally, the DBH column 518 includes a common metric known as diameterat breast height that may be estimated based on the height of thevegetation, height to live crown, among other factors.

As illustrated in FIG. 5, the processing performed at block 218 tocreate a tree list data file may include generating estimates about theattributes of vegetation from LiDAR data. For example, for each item ofvegetation represented in the tree list data file, the height to thelive crown and diameter at breast height are estimated using LiDAR datato generate the estimates.

Implementations of the present invention are not limited to the crownidentification routine 200 depicted in FIG. 2. Other routines mayinclude additional steps or eliminate steps shown in FIG. 2. Moreover,the steps depicted in FIG. 2 may also be performed in a different orderthan shown. For example, the creation of the tree list data file isdescribed with reference to FIG. 2 as being performed separate fromother steps of the routine 200. However, in practice, the tree list datafile may be populated dynamically as the LiDAR data is being processed.Thus, the crown identification routine 200 depicted in FIG. 2 providesjust one example of the manner in which an embodiment of the inventionmay be implemented.

Now with reference to FIG. 6, a species identification routine 600 foridentifying the species of vegetation based on LiDAR data will bedescribed. In one embodiment, the species identification routine 600 isconfigured to perform processing in conjunction with the crownidentification routine 200 described above with reference to FIG. 2. Inthis regard, LiDAR data associated with individual items of vegetationis analyzed in order to obtain species information.

As illustrated in FIG. 6, the species identification routine 600 beginsat block 602, where a geographic region is identified where a set ofLiDAR data was collected. As described in further detail below, and inaccordance with one embodiment, aspects of the present invention usespecies attribute templates created from samples collected in aparticular geographic region to identify species information. Thus, thespecies identification routine 600 identifies the geographic region fromwhich LiDAR data was collected so that a comparison may be performedusing an appropriate species attribute template. In this regard, thegeographic region where a set of LiDAR data was collected is readilyknown and may be represented in the LiDAR data itself. For example, whenthe raw LiDAR data is collected, information may be included in a binary.LAS file to identify the geographic region where the LiDAR scanning isbeing performed.

At block 604, an individual item of vegetation such as a tree, bush,etc., is selected for species identification. In one embodiment, aspectsof the present invention sequentially select individual items ofvegetation and identify the species of the selected item. For example,the crown identification routine 200 described above with reference toFIG. 2 generates a tree list data file. Each record in the tree listdata file contains location information and other data describingattributes of an individual item of vegetation. The speciesidentification routine 600 may sequentially select records representedin the tree list data file and perform processing to obtain speciesinformation about an item of vegetation represented in a selectedrecord.

As further illustrated in FIG. 6, at block 606, a comparison isperformed to determine whether the item of vegetation selected a block604 is from a hardwood or conifer species. As mentioned previously,aspects of the present invention may be used to identify the species ofa selected item of vegetation. In this regard, those skilled in the artand others will recognize that hardwood species (Alder, Birch, Oak,etc.) have less foliage on average than conifer species (Douglas Fir,Noble Fir, etc.). As a result, hardwood species also have less surfacearea to reflect electromagnetic waves. Thus, the average intensity inreturn signals is largely a function of the amount of foliage on a treeand provides a highly reliable indicator as to whether a tree is from ahardwood or conifer species.

As mentioned previously with reference to FIG. 2, the intensity ofreflected return signals is provided from the raw LiDAR data that isprocessed by aspects of the present invention. Thus, in one embodiment,a comparison is performed, at block 606, to determine whether theaverage intensity of the return signals generated from an item ofvegetation is above or below a threshold that is used to differentiatebetween conifer and hardwood species. If the average intensity is belowthe pre-determined threshold, than the species identification routine600 determines that the selected item is a hardwood species. Conversely,if the average intensity is above the predetermined threshold, theselected item is identified as a conifer species.

At block 608, an appropriate species attribute template used to make aspecies determination is identified. In one embodiment, sample sets ofLiDAR data from different known species are collected in variousgeographic locations. From the sample data sets, attributes of thedifferent species may be identified and represented in one or morespecies attribute templates. For example, calculations may be performedthat quantify aspects of a tree's branching pattern, crown shape, amountof foliage, and the like. As described in further detail below, sampledata that is represented in a species attribute template may serve as a“signature” to uniquely identify a species. In any event, at block 608,the appropriate species attribute template that represents datacollected from known species is identified. In this regard, when block608 is reached, a determination was previously made whether the selecteditem of vegetation is from a hardwood or conifer species. Moreover, thegeographic region of the selected item of vegetation was previouslyidentified. In accordance with one embodiment, attribute templates arecreated that are specific to particular geographic regions andcategories of vegetation. For example, if the selected item is a coniferspecies from the western United States, a species attribute templatecreated from sample conifers in the western United States is selected atblock 608. By way of another example, if the selected vegetation is ahardwood species from the southern United States, a species attributetemplate created from sample hardwoods in the southern United States isselected at block 608.

As further illustrated in FIG. 6, at block 610, a comparison isperformed to identify the species of the selected item of vegetation.More specifically, an attribute of the item of vegetation selected atblock 604 is compared to the species attribute template identified atblock 608. As described in further detail below, the comparisonperformed at block 610 is configured to identify a species representedin the species attribute template that maintains the same or similarattributes as the selected item of vegetation.

For illustrative purposes and by way of example only, an exemplaryspecies attribute template 700 is depicted in FIG. 7. In this regard,the exemplary species attribute template 700 may be referenced, at block610, to identify a species from which sample LiDAR data was obtainedwith the same or similar attribute as a selected item of vegetation. Asillustrated in FIG. 7, the x-axis of the species attribute template 700corresponds to the total height of an item of vegetation represented asa percentage. Moreover, the y-axis corresponds to the number of LiDARpoints generating return signals that are higher in the crown than aselected location. In this regard, FIG. 7 depicts the distributions 702,704, 706, and 708 of sample LiDAR data collected from different speciesof vegetation.

The distributions 702-708 plot the number of LiDAR points generatingreturn signals that are higher in the crown than a selected verticallocation. In this regard, the species represented in distribution 702reflects LiDAR return signals starting at lower vertical locationsrelative to the species represented in distributions 704-708. Forexample, as depicted in distribution 702, LiDAR return signals startbeing generated for this species at approximately 30% (thirty percent)of the sample's total height. For the species represented indistributions 704-708, LiDAR return signals start being generated atrespectively higher vertical locations. The species attribute templateindicates that branches and foliage that generate return signals tend tostart at a lower location for the species represented in distribution702. In this regard, the species attribute template 700 describes onecrown attribute that may be used to differentiate between species. Morespecifically, the vertical locations where return signals are reflectedrelative to total height may be used to identify species information.However, those skilled in the art and others will recognize that thespecies attribute template 700 depicted in FIG. 7 provides an example ofone data set that may be used by aspects of the present invention toidentify species information for an item of vegetation.

Again with reference to FIG. 6, a determination is made at decisionblock 612 regarding whether additional items of vegetation will beselected for species identification. Typically, all of the items ofvegetation represented in a tree list data file are selected andprocessed sequentially. Thus, when each record in a tree list data filedata has been selected, the species identification routine 600 proceedsto block 614, where it terminates. Conversely, if additional items ofvegetation will be selected for species identification, the speciesidentification routine 600 proceeds back to block 604, and blocks604-612 repeat until all of the items of vegetation represented in thetree list data file have been selected.

While illustrative embodiments have been illustrated and described, itwill be appreciated that various changes can be made therein withoutdeparting from the spirit and scope of the invention.

1. A method of operating a computer system to process LiDAR data toidentify the species of an item of vegetation, the method comprising:identifying with the computer system, LiDAR data that are associatedwith an item of vegetation; determining with the computer system, anaverage intensity of the LiDAR data associated with the item ofvegetation; selecting with the computer system, a hardwood or coniferspecies attribute template based on the determined average intensity ofthe LiDAR data associated with the item of vegetation, wherein thespecies attribute templates store data collected from different species;and analyzing with the computer system, the LiDAR data associated withthe item of vegetation and the data in the selected species attributetemplate to identify a species that most closely matches the item ofvegetation.
 2. The method as recited in claim 1, wherein identifying theLiDAR data that are associated with the item of vegetation includesusing the computer system to determine whether the LiDAR data representreflected LiDAR return signals that are within an area allocated to adigital representation of the item of vegetation.
 3. The method asrecited in claim 1, wherein the species attribute templates store datafrom sample vegetation in a specified geographic region.
 4. The methodas recited in claim 1, wherein the species attribute templates storedata representing a number of LiDAR points generating return signalsthat are higher in a crown than a selected vertical location fordifferent species of vegetation.
 5. The method as recited in claim 1,wherein the data stored in a species attribute template is at least onedata element in a group consisting of data elements that describe abranching pattern, crown shape, diameter, height, height to live crown,and amount of foliage for an item of vegetation of a known species. 6.The method as recited in claim 1, wherein the computer system identifiesa species from the selected species attribute template by performing acomparison between attributes of the LiDAR data associated with the itemof vegetation and data stored in the selected species attribute templatethat represent attributes of known species.
 7. A computer system foridentifying the species of an item of vegetation, the computer systemcomprising: a memory that stores LiDAR data for a selected item ofvegetation and one or more species attribute templates that store datacollected from different species; a processor that is configured toexecute a sequence of programmed instructions that cause the processorto: identify coordinate positions and intensity values in the LiDAR datathat are generated from an item of vegetation in response to beingcontacted with a LiDAR laser pulse; identify a species of the item ofvegetation by selecting a species attribute template with data thatdescribes one or more attributes of a known species; comparingattributes of the LiDAR data that are generated from the item ofvegetation with the data stored in the selected species attributetemplate; and identifying a species of the item of vegetation bydetermining a species attribute template that stores data that mostclosely matches the attributes of the LiDAR data that are generated fromthe item of vegetation.
 8. The computer system as recited in claim 7,wherein the processor is programmed to execute instructions that causethe processor to: determine an average of the intensity values of theLiDAR data associated with the item of vegetation; and if the average ofsaid intensity values is at or above a threshold, determine that theitem of vegetation is a conifer species; and if the average of saidintensity values is below the threshold, determine that the item ofvegetation is a hardwood species.
 9. The computer system as recited inclaim 7, wherein the processor is programmed to execute instructionsthat cause the processor to select a species attribute template byidentifying a geographic region where the item of vegetation is locatedfrom data maintained in a data file.
 10. The computer system as recitedin claim 7, wherein species attribute templates are maintained that arespecific to different geographic regions.
 11. The computer system asrecited in claim 7, wherein the data represented in a species attributetemplate describes a branching pattern for different species ofvegetation.
 12. The computer system as recited in claim 7, wherein thespecies attribute template stores the number of LiDAR points generatingreturn signals that are higher in the crown than a selected verticallocation for different species of vegetation.
 13. A computer-readablemedium bearing computer-executable instructions that, when executed by aprocessor, cause the processor to carry out a method of processing LiDARdata to identify the species of an item of vegetation, the methodcomprising: identifying LiDAR data that are associated with the item ofvegetation; determining an average intensity of the LiDAR data;selecting a species attribute template that stores data collected fromdifferent species based on whether the average intensity is above orbelow a threshold that differentiates between conifer and hardwoodspecies; identifying a species of the item of vegetation from theselected species attribute template by comparing one or more attributesof the LiDAR data associated with the item of vegetation to the datastored in the species attribute template to determine a species with adata attribute that most closely matches an attribute of the LiDAR dataassociated with the item of vegetation.
 14. The computer readable-mediumas recited in claim 13, wherein the instructions that cause theprocessor to identify LiDAR data that are associated with the item ofvegetation include instructions that cause the processor to determinewhether the LiDAR data correspond to LiDAR return signals that arewithin an area allocated to a digital representation of the item ofvegetation.
 15. The computer readable-medium as recited in claim 13,wherein the species attribute templates are generated from samplevegetation in a specified geographic region and wherein the instructionsthat cause the processor to determine whether the average intensity ofthe LiDAR data is above or below a threshold includes instructions thatcause the processor to identify data from a file that represents ageographic region where the LiDAR data for the item of vegetation wereobtained.
 16. The computer readable-medium as recited in claim 13,wherein the species attribute template stores a number of LiDAR pointsgenerating return signals that are higher in the crown than a selectedvertical location for different species of vegetation.
 17. The computerreadable-medium as recited in claim 13, wherein the data represented ina species attribute template is at least one data element in a groupconsisting of data element that describe a branching pattern, crownshape, diameter, height, height to live crown, and amount of foliage forknown species.
 18. The computer readable-medium as recited in claim 13,wherein the instructions that cause the processor to identify a speciesfrom the selected species attribute template include instructions thatcause the processor to perform a comparison between attributes of theLiDAR data associated with the item of vegetation and data stored in theselected species attribute template that represent attributes of knownspecies.